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Void-and-Cluster Sampling of Large Scattered Data and Trajectories.

Tobias Rapp, Christoph Peters, Carsten Dachsbacher

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    We developed a novel statistical sampling method for data reduction. This technique ensures optimal spatial distribution and adaptive sampling for complex data, enabling efficient visualization and analysis.

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    Area of Science:

    • Computer Science
    • Data Science
    • Scientific Visualization

    Background:

    • Scattered data presents challenges for efficient storage and visualization.
    • Existing data reduction techniques may not adequately capture spatial distribution or adapt to data complexity.

    Purpose of the Study:

    • To introduce a novel statistical sampling technique for scattered data reduction.
    • To enable adaptive sampling based on data density and ensure optimal spatial distribution.
    • To support progressive data loading and continuous level-of-detail representations.

    Main Methods:

    • Void-and-cluster sampling technique for optimal spatial distribution (blue noise property).
    • Adaptive sampling to density functions for prioritizing complex data regions.
    • Extension to time-dependent trajectories (e.g., pathlines) using an iterative approach.
    • Development of a local and continuous error measure for representation quality.

    Main Results:

    • The technique generates a representative subset with blue noise properties.
    • Adaptive sampling allows denser sampling in high-complexity multivariate value domains.
    • Implicit sample ordering facilitates progressive loading and level-of-detail.
    • The error measure effectively guides sampling and quantifies representation accuracy.

    Conclusions:

    • The proposed void-and-cluster sampling is an effective data reduction technique for scattered and time-dependent data.
    • The method offers advantages in spatial distribution, adaptive complexity handling, and progressive visualization.
    • The introduced error measure provides valuable insights into sampling quality, performance, and scalability.